High throughput cell culture technology as a route to improved process understanding. Seth Rodgers, CTO Bioprocessors Jan 29, 2007

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Delilah Lambert
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1 High throughput cell culture technology as a route to improved process understanding Seth Rodgers, CTO Bioprocessors Jan 29,

2 Building process understanding during process development Although bioprocess development is sometimes considered a separate activity form manufacturing and quality control, it is safe to say that process design decisions often have major impact on the finished product- this is the root of process understanding. Process Development Process understanding Manufacturing API QC Process improvements listening to the process and interpreting In the best case, process development could provide not just process parameters but understanding: identifying, the sources of variability and mapping the design space to allow interpretation/prediction of process outcomes, serving as a foundation for continuous improvement 2005 BioProcessors info@bioprocessors.com (781)

3 High-Throughput Cell Culture System Requirements Deliver data at least as predictive as current models - to guide optimization and identify and rank sources of process variability Sustain cells by maintaining sterility while controlling temperature, O2, CO2, ph, agitation to a degree comparable to bench top bioreactor systems Support common bioreactor operating modes by automatic addition or removal of fluids or cell suspension Enable samples to be taken at comparable throughput for offline analysis Metabolites Product titer and product quality Deliver cost and time savings over conventional platforms by automation or routine tasks Automatic operation with minimal operator supervision Practical, user-friendly tools for designing experiments and handling data 2005 BioProcessors info@bioprocessors.com (781)

SimCell MicroBioreactor Array 6 micro-bioreactors per array. Working volume: ~700 µl. Fluidic ports and channels for inoculation, feeds, ph adjustment and sampling.

4 SimCell MicroBioreactor Array 6 micro-bioreactors per array. Working volume: ~700 µl. Fluidic ports and channels for inoculation, feeds, ph adjustment and sampling. Culture monitoring of biomass (OD), ph (immobilized sensors) and DO (immobilized sensors) by optical interrogation of micro-bioreactors. Proprietary gas permeable materials result in k L a ~ 10 hr -1 for oxygen and ~ 25 hr -1 for CO 2. Supports mammalian cell cultures to greater than 2e7 cells/ml. Simulates batch and fed-batch production modes. Experimental factors such as media composition, inoculation density, ph and feeds can be adjusted at the micro-bioreactor level BioProcessors info@bioprocessors.com (781)

SimCell Automated Management System Incubators One to five per system. T, CO 2, O 2 and agitation control. Sensing module Total biomass by OD. ph by immobilized sensors. DO by immobilized sensors.

5 SimCell Automated Management System Incubators One to five per system. T, CO 2, O 2 and agitation control. Sensing module Total biomass by OD. ph by immobilized sensors. DO by immobilized sensors. Sampling module Sample removal to well plate. Capable of dilution with single diluent (PBS). Capable of volumetric dilution or dilution to specific cell density in well plate or MBR. Dispensing module One to eight pumps. Real-time mixing at point of delivery. Fluid sources may be swapped in between cycles for increased capacity BioProcessors info@bioprocessors.com (781)

6 From Factorial Design to Relevant Data Experimenter defines process parameters and overlays information from factorial design Export to automated system and run a maximum of 1260 cell culture experiments.* *in practice, maximum capacity depends on specific experiment design process parameters 2005 BioProcessors info@bioprocessors.com (781)

7 SimCell enables full-factorial optimizations, revealing interactions missed in a one factor at a time approach Process 3 Process The global optimum could be anywhere in here 1 Clones Today s cost and throughput constraints dictate piecewise, serial optimization First clone selection, then media development, then process parameters This process is not only lengthy; but, can miss important interactions between variables and thus the true BioProcessors info@bioprocessors.com (781) Clones SimCell s high throughput allows a more complete exploration of the design space Multi-objective optimization is also possible Reduced process variability Easy purification Improved product quality 7

Multi-factorial optimization of protein production B A Summary Just a few weeks to map parameter space- that would have taken the customer more than 1 year by conventional means 84% increase in yield

8 Multi-factorial optimization of protein production B A Summary Just a few weeks to map parameter space- that would have taken the customer more than 1 year by conventional means 84% increase in yield by decreasing temperature, increasing ph Scalable to 1,000 liter production vessels Significant improvement n process yield at lower cost and shorter time 2005 BioProcessors info@bioprocessors.com (781)

9 Conclusions Process understanding is the key to realizing the benefits of quality by design Process understanding begins in process development, where many critical decisions are made. Model systems are indispensable tools, and increasing demands for data will be difficult to meet with current platforms. A high-throughput cell culture system presents a possible solution if the data is of sufficient quality to predict process outcomes. BioProcessors SimCell system represents one possible solution that combines high throughput with highly representative data BioProcessors info@bioprocessors.com (781)

10 2005 BioProcessors (781)

11 Outline The role of model systems in process understanding Multi-factorial experimentation is the tried and true route to increased process understanding, but requires high throughput. A high throughput model bioreactor system Conclusions 2005 BioProcessors info@bioprocessors.com (781)

12 Process understanding and quality by design Process understanding is a prerequisite for quality by design. A process is well understood when*: All critical sources of variability are identified and explained Variability is managed by the process Product quality attributes can be accurately and reliably predicted Subsequent leveraging of process understanding allows identification of sources of variability, in turn allowing Reduced initial process variability (Quality by design) A risk-based approach to regulatory oversight Effective feedback control Continuous variability reduction The big idea: quality cannot be tested into products, it must be there by design *PAT- A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance Ali Afnan, PhD, Office of Pharmaceutical Science, FDA 2005 BioProcessors info@bioprocessors.com (781)

The role of model systems Experiments with the systems that provide the best data,

Model systems are universally used, but represent a compromise: reduced time and

Process understanding Model systems: Data is the product Real system: The API is the

needed to achieve quality by design? Can we get there without breaking the bank?

13 The role of model systems Experiments with the systems that provide the best data, and the most understanding, i.e. production bioreactors themselves, are very time consuming and expensive. Model systems are universally used, but represent a compromise: reduced time and expense in exchange for imperfect data, which leads to imperfect understanding Process understanding Model systems: Data is the product Real system: The API is the product What will it take to get to the increased level of process understanding needed to achieve quality by design? Can we get there without breaking the bank? 2005 BioProcessors info@bioprocessors.com (781)

14 Can current models provide deeper process understanding? A strategy of multifactorial experimentation is the tried and true route to process understanding and optimization 1000 s 100 s Well plate High Quality, High Quantity Most attempts at large scale factorial experimentation have focused on stretching existing models: Throughput Experimental capacity per researcher 10 s Flask Upgrading well plates Multiplexed flasks 1 s Bioreactor Multiplexed bioreactors Maybe there is a better way Low Quality Ability to predict manufacturing bioreactor performance High 2005 BioProcessors info@bioprocessors.com (781)

15 SimCell On-Line Measurements: Cell Density Measured using Optical Density at 633 nm on Sensor Station OD is linear to 2.2 Working OD range is extended by dynamic neutral density filtering accurate measurements up to 50M cells/ml have been demonstrated OD vs. cells/ml curve is specific to cell type OD yields total intact cells: live + dead biomass 1x10 7 9x10 6 8x10 6 7x10 6 6x10 6 5x10 6 4x10 6 3x10 6 OD calibration curve Each data point is an average of 9 measurements (3 chambers x 3 OD readings/chamber) OD calibration based on handcounted cells (> 95% viable) and serial dilution of stock cell suspension (8x10 6 cells/ml) 2x10 6 1x Error bars are +/- 1 std. dev. (+/- 16% variance) OD 633nm 2005 BioProcessors info@bioprocessors.com (781)

Measurements: Immobilized ph Sensors ph Sensor Composition

polymerization Response is independent of media Precision is < 0.

16 Measurements: Immobilized ph Sensors ph Sensor Composition Hydrogel (Water-swellable polymer) Covalently bound dye fluorescent pyrene derivative. Sensor manufactured by screen-printing, followed by UV polymerization Response is independent of media Precision is < 0.06 (±3 std. dev.) over the ph range BioProcessors (781)

17 SimCell On-Line Measurements: Dissolved Oxygen (DO) Measurement Oxygen-sensitive dye (platinum porphyrin derivative) Excitation of dye yields emissive triplet state. As [O 2 ] increases, dye emission is quenched and τ F decreases τ F is correlated to phase shift (φ) between modulated excitation and emission signals po 2 (% air saturation) phase Signal (arbitrary units) Excitation Emission φ time Phase shift between excitation (blue) and emission (red) signals. Correlation of φ to DO: error bars are +/- 1SD (+/- 10% variance) BioProcessors info@bioprocessors.com (781)

18 Measurement and Control: Maintaining ph Setpoints 3 ph setpoints 18 subprotocols 9 μbr/subprotocol ph adjusted 2x/day Chart shows average ph for each subprotocol over the course of the experiment BioProcessors info@bioprocessors.com (781)

Application across platforms and processes The central question is To what extent is the MBA performance a predictor of the bioreactor result?

are that is can be constructed independent of platform, process cell line, etc.

19 Application across platforms and processes The central question is To what extent is the MBA performance a predictor of the bioreactor result? The R^2 statistic is a wellestablished way to quantify the answer to this question, computed by constructing ordered pairs of MBA and reference model system results Advantages of R^2 are that is can be constructed independent of platform, process cell line, etc. This graphic shows results over many cell lines, processes and vessels, predictive power might be even better for data with these factors kept constant Reference vessel Titer R 2 for 2006 client evaluation projects R 2 =0.89 MBA Titer 2005 BioProcessors info@bioprocessors.com (781)